KNOWLEDGE MANAGEMENT DIFFUSION AMONG SAUDI SMALL AND MEDIUM-SIZED
ENTERPRISES (SMES)
Naief G Azyabi
Faculty of Business Administration Jazan University, Saudi Arabia
Accepted Date: 26 April 2019
ABSTRACT
The purpose of this research is to study the contextual factors and their contextual factors consist of 1) technological factors, such as IT support and IT effectiveness; 2) organizational factors such as top management support, reward system and sharing culture; 3) and environmental factors in terms of competitive pressures. This also includes the stage-stage KM diffusions within the management perspective (KM adoption and implementation) in business environment. The sample research included 91 SMEs under the level of management including senior managers (Stakeholders, middle level managers and vice president) who were tasked with testing the relationship between the variables in the study by applying the model of partial least and thus the factors such as KM adoption variances, implementations and critical review considerations were evidenced through the research. IT support became an important support shift for KM adoption and also the is positive correlation between KM diffusion levels as well as the variances that support the entire process of determining the correlations between these factors. IT support represents an important factor within itself that help to transform the IT system through an integral approach, between link to knowledge economy and enhanced knowledge in systems development.
Keywords: knowledge Management, SMEs, Saudi Arabia, IT, KM diffusion
INTRODUCTION
Despite the fact that Saudi Arabia is faced with many challenges
2015). The Saudi Arabian market seems to have less interest on performing (KM), and hence hindering development of its SMEs (Dalkir et al, 2011).
KM.
research attempts to answer the following question:
implementation of knowledge management in Saudi SMEs?
There is a need to critically review literature related to thriving and development of SMEs in Saudi Arabia with regards to KM practice and reviewing the related literature, the research model has been developed, research hypotheses have been formulated and the survey items have been
the necessary recommendations and conclusion were presented.
LITERATURE REVIEW
The research concept about KM diffusion is provided in the theoretical
analytically theorize the issue at hand, there is a need to make a review of
Concept of Knowledge Management and Knowledge Management Diffusion
Knowledge management refers to the practice of obtaining, developing, presenting, and utilizing organizational knowledge in an effective manner (Tsai et al, 2006). The process of KM includes sharing organizational knowledge and since it is a multi-disciplinary approach, it contributes to accomplishing organizational objectives by disseminating best practices and Development Report, KM is based on a systematic approach for using et al, 2006). KM involves improving productivity and dynamic capability (Ruggles, 2009). In a critical analysis by Zhang et al., (2009), KM is about of information and knowledge between the employees.
KM diffusion enabled research to be conducted in Australian organizations, and qualitative research was basically used for interviewing and it focused on the development of a relevant model for the adoption and diffusion of Knowledge Management. As a result, three major factors
study presented its model in regards to the three variables. The research comes to KM diffusion. In accordance with Huang et al, (2007), the concept of knowledge diffusion presents better practices in the community. There was development of an algorithm for depicting the procedure of knowledge diffusion amongst workers. An algorithm helped to highlight challenging comprehension as asserted by (Chen et al, 2009).
In accordance with the report advanced by Huang et al, (2007), the application of knowledge sharing and discussion can enhance its diffusion.
Results of the study depicted that changes within the knowledge work The involvement of community members in knowledge practice increases the awareness of better knowledge management and control. However, measuring the development of knowledge among workers involves
budgeting and time constraints, which leads to planning, analyzing, predicting, and enhancing the KM initiatives (Du Plessis, 2007).
Small and Medium Sized Enterprises (SMEs) in Saudi Arabia
The banking industry of Saudi Arabia has presented an opportunity for ninety percent (90%) of the registered businesses within the country have
when it comes to the development strategy of the country. In a related report after involving such a high percentage of businesses in the SMEs sector, the country’s GDP remains at a lower rate (Zamberi, 2012).
With regards to Ahmad et al, (2010), most SME programs and proposals have been competing throughout the country, and that indicates that there is an essential need for developing a new SME support organization. The factors of the SMEs in the country, there is need for developing the national SME authority for affectivity (Ahmad et al, 2010). Saudi Arabia has grown
and thirty three percent (33%) to the GDP of the country (Achoui, 2009).
Product as described by the Capitals Group International, (2011). Saudi Arabia has been facing challenges within the SME sector that have presented obstacles in order for the enterprises to be competitive and operate for better that contribute to meeting the requirements of SMEs has presented a shorter According to statistics by Hertog, (2010), total enterprises in Saudi Arabia comprise of ninety three percent (93%), while commercial registrations compose of ninety five percent (95%), and industrial
establishments make up to seventy one percent (71%). While of these forty seven percent comprise of hotel businesses, other small percentages are made up of the manufacturing sector, social services as well as construction.
As a result, the SMEs contribute less towards the growth of Saudi Arabia’s GDP (Ryan, 2011).
that a small amount of the SMEs achieve success when it comes to high businesses (Sidika, 2012).The study highlights the positive relationship can of SMEs. Even though there is a positive relationship here, the connection intervening constructs within the two mentioned constructs. The results have presented 5 second-order constructs in terms of being mediating roles These are entrepreneurial orientation, innovative capacity, market orientation, innovative performance, and organizational search. The framework only contributes in terms of SMEs highlighting the impact framework, but the KM factor had not been identified. In the study conducted by Lin (2014), a research model was developed for identifying and environmental aspects in terms of KM, innovation diffusion theory, and technology framework. These factors included IT support and effectiveness, top management support, sharing culture, rewards system, and competitive
adoption and implementation in the SMEs (Chen et al, 2007). The research for analyzing the connection amongst the constructs of the research model the technological, environmental, and organizational factors comprise of different impacts when it comes to the stages of KM adoption and implementation. Information Technology supports presented a strong impact on the KM adoption stage and sharing culture had the strongest impact on
the KM implementation stage. IT support showed a positive relationship with the KM implementation after adoption (Brachos et al, 2007).
KM diffusion involves the managers for the purpose of investing their time and efforts in order to function and process IT support and knowledge related work activities because presentable IT deployment in terms of KM, it contributes in helping SMEs to enhance towards a knowledge-based society (Brachos et al, 2007). This is an essential factor when it comes to the contemporary knowledge economy. The results of this study are based KM diffusion in terms of a diverse dataset relatively to the some of the isolated SME cases.
RESEARCH MODEL AND HYPOTHESES
Figure 1. Research Model
stages of KM diffusion, as mentioned above. The study deemed the stages of KM adoption and KM implementation as the dependent variables of the study (Zhang et al, 2009). To investigate this aspect it is necessary to assess
Organizational, and Environmental since the literature portrays evidence of their impact on KM diffusion stages (Brachos et al, 2007).
KM Adoption Technological Contexts
Organizational Contexts
Environmental Contexts
KM Implementation
According to Zack et al (1999) IT support is based on three major digital items as well as storing of data. It is also related to related studies al., (2006) indicates that the use of IT in a greater perspective is important research works of Tan, (2011) KM activities are enhanced through critical decision-making processes that also establishes a link between IT and the knowledge that is made available to professionals, employees and other stakeholders which hence enable them to facilitate KM diffusion in the functioning of SMEs. In this consideration, the argument amount to the
be utilized in Real time. This provides this research with a link between SME employees and the use of IT which is fully compliant with search, further works in accessing knowledge. This therefore, includes SMEs quality successes of medium and small scale enterprises. According to Nurach et al., (2012) high IT effectiveness as well as better access to information is adoption and application. Therefore, the research hypothesis will be:
H2: IT effectiveness directly influences KM adoption and KM implementation.
ORGANIZATIONAL CONTEXT
Therefore, there are KM activities that need to be well engaged with top
to ensure that KM is successfully implemented. Successful innovation is established by underlining the trends about the role that is played by management in ensuring that employees participate in knowledge search, compilation and using as part of result-oriented search. This implies that a strong management at the higher level will constitute mature KM activities and thereby this will fully support and recreate KM diffusion (Choi and
KM implementation.
Culture is an important factor that link employees and IT and the sharing approach relevant to this study involves building knowledge search according to Hoegl et al., (2003) is derived from KM activities and functions and thus sharing knowledge forms an important part of its development. The sharing of culture is important for incorporating knowledge with relative the adoption and implementation of KM activities. The suggestion based
H4: sharing culture directly influences KM adoption and KM implementation.
To effectively promote KM projects, there are major approaches that SMEs could apply and this includes bonuses, job security and salary
H5: Reward system directly influences KM adoption and KM implementation.
competitive pressures as well as the competition in the markets. Therefore, threats that are attributed to adoption of various levels of KM and its
better deployment of KM which institutes the overall aspect of competitive
implementation.
RESEARCH METHODOLOGY
Research Design and Approach
The quantitative research design was followed that is quantitative in nature and focuses on the pre-planned method to collect information to show the relation between and among different variables (David, 2014). On the other hand, this research design suggests the right selection of approaches qualitative, and quantitative strategies are included (Mackay et al, 2013).
Data Collection
Data for the study is gathered using questionnaire. It is very effective
the other hand, it is not helpful for those studies where the objectives of the investigations require the data in either subjective or objective form for convenient interpretation. This research has adopted the quantitative method to verify the hypotheses. This is because, in order to accomplish the hypothesis based investigations, quantitative research approach is required that shows the empirical evidence through objective information gathered from participants (Greiner et al., 2007).
Measures
The literature review provided the necessary measurement which
KM diffusion stages as well as the KM-aligned objectives. The important through guided structural estimation of variable constructs.
In opposition, a pre-testing of the survey was also conducted to check the instrument clarity and validity of language/wording used in questions
a basis for correcting the mistakes to construct the best measures. A total of 91 questionnaires were distributed among the senior managers (owner,
DATA ANALYSIS
In order to analyze the data, the regression and correlation were software is quite effective in determining right kind of relationship between variables. The analysis is conducted by computing responses under heading of one variable. This is necessary to see the effect of factors on the dependent variables. Regression analysis is conducted by implying linear regression technique to assess hypothesis using Beta, R Square and Correlation values.
Data Analysis and Results
along with several other components to test hypothesis. Research model of the study contain two stages accommodating dependent, and independent variables. Initially proposed hypothesis will be tested by identifying effects of independent variables on mediating variables (Lawrence, 2005). In this case the mediating variables are pretended to be dependent and linear factors stated in the research model are quite interlinked as stated in previous studies. However, to validate their relationship in this research because model can’t move forward because there are hindrances in the
effectiveness correlation.
Dependent variables: The measures for dependent variable fall into two categories: KM adoption and KM implementation. It also focuses on the core KM activities that include generating knowledge, absorbing knowledge The relationship strength in a correlation analysis is indicated between the values of 0 and 1. Strength of the relationship between the dependent is closer to the value of 1.000. A weak relationship between variables is
null hypothesis will equal to zero to illustrate no association between the dependent and independent variables, while the alternative hypothesis will KM adoption and KM implementation together compute the KM Diffusion. The effectiveness of KM diffusion produces effective and desired results in the performance of SMEs. To verify this relationship, the correlation tool has been applied on the data gathered for KM adoption and KM implementation.
Table 1. Bivariate Correlation between KM adoption and KM implementation KM Implementation KM Adoption KM
Implementation Pearson Correlation 1 .811**
Sig. (2-tailed) .000
N 91 91
KM Adoption Pearson Correlation .811** 1
Sig. (2-tailed) .000
N 91 91
adoption and KM implementation variables. This indicates that a strong
Table 2. Pearson Correlation of KM diffusion and Independent Variables
KM Diffusion IT Support IT
Effectiveness Top Management
Sharing Knowledge
Reward system
Competitiveness pressure KM
Diffusion
Pearson Correlation
1 .924** .690** .387** .527** .527** .383**
Sig. (2-tailed) .000 .000 .000 .000 .000 .000
N 91 91 91 91 91 91 91
IT Support
Pearson Correlation
.924** 1 .572** .366** .435** .406** .232*
S i g . (2-tailed)
.000 .000 .000 .000 .000 .027
N 91 91 91 91 91 91 91
IT
Effectiveness Pearson
Correlation .690** .572** 1 .278** .491** .267* .483**
S i g . (2-tailed)
.000 .000 .008 .000 .010 .000
N 91 91 91 91 91 91 91
Top Management
Pearson Correlation
.387** .366** .278** 1 .657** .481** .557**
S i g . (2-tailed)
.000 .000 .008 .000 .000 .000
N 91 91 91 91 91 91 91
Sharing
Knowledge Pearson
Correlation .527** .435** .491** .657** 1 .476** .680**
S i g . (2-tailed)
.000 .000 .000 .000 .000 .000
N 91 91 91 91 91 91 91
Reward system
Pearson Correlation
.527** .406** .267* .481** .476** 1 .463**
Sig. (2-tailed) .000 .000 .010 .000 .000 .000
N 91 91 91 91 91 91 91
Competitiveness
pressure Pearson
Correlation .383** .232* .483** .557** .680** .463** 1
Sig. (2-tailed) .000 .027 .000 .000 .000 .000
N 91 91 91 91 91 91 91
relationship between dependent and independent variables. Hypothesis 3, 4, and 5 has been tested by computing variables separately using Pearson Correlation.
KM adoption and KM implementation jointly form KM diffusion that is evident in Table 2, 3 and 4.
Hypothesis 1: IT Support Variable
H0: IT support does not directly influence KM adoption and KM implementation
between the dependent variable and the KM diffusion and the independent variable of IT support is 0.924. The KM Diffusion and IT support independent the null hypothesis can be rejected and the alternative hypothesis accepted since the p-value is less than 0.01. Therefore, IT support is an important variable that contributes to the diffusion of knowledge in SMEs, especially where huge infrastructure is not mandatory.
Hypothesis 2: IT Effectiveness Variable
KM implementation
HI: IT effectiveness directly influences KM adoption and KM implementation
A strong positive relationship is found between KM Diffusion and IT
hypothesis is accepted.
Hypothesis 3: Top Management Support
and KM implementation KM implementation
The alternative hypothesis of this independent variable can be accepted support variable is 0.387. This is a weak correlation to the KM diffusion, illustrating that the support of the top management in SMEs is not a priority to ensuring the adoption and implementation of knowledge in the SME.
Hypothesis 4 and 5: Sharing Culture and Reward System Variables
The independent variables of sharing culture and the reward system had the same level of correlation to the KM diffusion, with a positive
within an SME and also the reward system accorded to employees equally enable the diffusion of knowledge within the SME.
Hypothesis 6: Competitive Pressure
and KM implementation implementation
According to the correlation analysis conducted, the independent variable of competitive pressure has a weak correlation to the knowledge management diffusion of the research. Consequently, competitive pressure of knowledge management within the enterprise.
Regression Analysis
Linear Regression test is used to analyze the relationship between the dependent and independent variables. Table 3 (model Summary) shows the variability of responses through the regression line. The overall simple correlation between the KM diffusion and the independent variables is 0.960.
This indicates a strong positive overall correlation between the dependent
the independent variables. Consequently, the successful diffusion of KM strongly depends on IT support and effectiveness, the support of the top reward system.
Table 4 shows the ANOVA table for the regression analysis of the knowledge management research model. This table has the purpose of
of 0.05. Due to the strength of the correlation between the dependent and
Table 3. Model Summary R R Square Adjusted R Square
Std. Error of the Estimate
1
.960a .922 .916 2.10561
a. Predictors: (Constant), Competitiveness, IT Support, Reward, Top Management, IT Effectiveness, Sharing Knowledge
Table 4. ANOVA Analysis Model
Sum of Squares
Df Mean Square F Sig.
1 Regression 4389.709 6 731.618 165.017 .000a
Residual 372.422 84 4.434
Total 4762.132 90
a. Predictors: (Constant), Competitiveness pressure, IT Support, Reward system, Top Management, IT Effectiveness, Sharing Knowledge
b. Dependent Variable: KM Diffusion
Model
Unstandardized Standardized
T Sig.
B Std. Error Beta
1 (Constant) 11.499 2.452 4.690 .000
IT Support 1.882 .107 .742 17.629 .000
IT Effectiveness .753 .165 .196 4.566 .000
Top Management -.206 .115 -.078 -1.789 .077
Sharing Knowledge
.108 .122 .044 .887 .377
Reward .589 .135 .165 4.347 .000
Competitiveness .256 .229 .053 1.118 .267
a. Dependent Variable: KM Diffusion
has the purpose of detailing the values of the regression line. The table provides essential information that allows the prediction of the dependent
support, IT effectiveness and reward system as independent variables are
KM Diffusion = 11.50 + (1.88*IT support) + (0.75*IT effectiveness) + (0.59*Reward)
and the dependent variables of KM adoption and KM implementation..
Results from the regression analysis also point out that all the independent variables, apart from top management variable, have a positive relationship with the dependent variable of KM diffusion. The results suggest that an increase in top management support will lead to a 0.206 decrease in knowledge management adoption and implementation. However, analysis of the negative relation between top management and KM diffusion is These results support the correlation results that show the strength of the relationship between independent and dependent variables of the research.
the various hypotheses of the research model, therefore, can be supported using these analytical results.
DISCUSSION AND IMPLICATIONS
The results obtained from this study provided the necessary benchmarks which are evidently necessary to mainstream different factors of KM adoption and implementation. The factors considered in this case
between independent variables (called technological factors) and KM diffusion, i.e. KM adoption and KM implementation. The technological environment in which present SME operates brings many challenges in regards to innovation, which is considered essential for SMEs suitability.
Hence KM is vital factor in providing SMEs with strategic tools for
top management support, reward system and knowledge sharing culture in SMEs. The reward system and sharing culture in an organization have Surprisingly, the support of the top management of the SMEs is not nearly as important as ensuring the satisfaction, loyalty and commitment of employees in SMEs.
Competitive pressure on SMEs from the market has a positive impact on KM diffusion within SMEs. SMEs will ensure effective KM adoption and implementation in order to have a competitive advantage in the market.
comprehensive reward system and knowledge sharing culture for purposes of ensuring survival in a competitive market.
Data analysis in a pre-section of the study has given numerical level of intensity. This is measured in the form of correlation to show whether independent variables on dependent variables. Ultimately, the performance of SMEs will improve with the better structure of Knowledge Management
combined effect is greatly affected by individual forces in the shape of independent variables.
CONCLUSION
Conclusively therefore, KM is a factor that plays a pivotal role in trying to enhance better organizational behavior as well as creating the best environment for industrial thriving and development. This study focused on
engaged through critical reviews of IT support, IT effectiveness, reward systems, sharing culture, top management support and competitiveness pressure which are all enshrined into a system that reflects a major as shared longitudinal data and measurement scales provided the necessary diffusion and adoption.
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